Go beyond CTR with 6 AI-powered SEO discoverability metrics

Thanks to AI-generated answers, CTRs are failing fast, and even page-one rankings no longer guarantee clicks. Google’s top organic results saw a 32% CTR drop after AI Overviews launched, plummeting from 28% to 19%. Position #2 fared even worse, with a 39% decline. Meanwhile, 60% of searches in 2024 ended without clicks; also, the projections show zero-click searches could surpass 70% by 2025. What does this mean for measuring success?

The era of measuring SEO success purely through traffic metrics is coming to a standstill. AI systems like ChatGPT, Perplexity, and Google’s AI Overviews and AI Mode deliver instant answers; therefore, brand visibility increasingly happens without clicks. Marketers will turn to AI-enabled discoverability metrics that capture actual influence. 

This guide explains why it’s important to go beyond CTR. It reveals six AI metrics that predict success in AI-driven search, plus strategies to measure and optimize your visibility when clicks disappear. 

How does this disrupt traditional SEO? 

Google’s AI Overviews (and similar features on Bing, etc.) generate a concise, multi-sentence answer at the top of the results page. These summaries cite source links, pulling content from high-ranking pages and knowledge panels. To the user, this is convenient: you get an instant answer without scrolling.  

For marketers, however, it means the user’s query can be resolved on-page. From the publisher’s standpoint, these overviews satisfy search intent without generating a click, effectively extending the trend of zero-click searches. In other words, the page may be used (quoted in the answer) but not clicked.  

AI Overviews significantly accelerate zero-click behavior. A finding suggests that zero-click searches jumped from ~24% to 27% year-on-year in early 2025. A Bain survey reports that about 60% of searches end without users clicking through to another site. 

In practice, organic listing CTRs are under siege. Top-ranked pages are losing share because AI answers capture attention. We see that Google’s new summarization features are faster and more convenient, which might mean that these become the default way people search, shifting discovery away from traditional blue links. 

Evidence of a drastic CTA decline

Multiple independent studies show massive CTR drops wherever AI summaries appear. Recent industry data paints a stark picture of CTR decline across prominent search positions:   

This data, compiled from over 200,000 keywords across 30+ websites, coincides directly with Google’s aggressive AI Overview expansion. From just 10,000 triggering keywords in August 2024, AI Overviews now appear for over 172,000 queries by May 2025. In practical terms, a top-ranking page that used to draw nearly three out of 100 searchers now gets under one.

Paid search is hit, too. In one study, paid CTR roughly halved in queries with AI Overviews: dropping from 21.27% without an AIO to 9.87%. In other words, even ads share the fate of organic results, AI answers grab a lot of the click-through “real estate.”  

These shifts mean many queries that once sent healthy website traffic now keep users on the SERP. In short, AI Overviews are dragging down CTRs significantly across positions and query types. 

AI Overviews are the zero-click accelerator 

Google’s AI Overviews represent more than a UI change because they reshape user search behavior. When AI Overviews appear:  

• Organic CTR drops 70% (from 2.94% in the previous year to 0.84% in 2025)  
• Paid CTR falls 54% (from 21.27% to 9.87%)  
• Featured content gets answered directly without requiring website visits  

Major publishers report even more dramatic impacts. MailOnline found that CTRs plummeted to under 5% on desktop and 7% on mobile when AI Overviews were present, a blow to traffic-dependent business models.  

These drops aren’t limited to one sector. Industries heavily reliant on informational queries (health, science, how-to guides, etc.) report the biggest hit. For instance, Semrush notes that sites in health and science categories see the most AI Overview inclusion and significant organic traffic losses.  

AI Overviews primarily trigger informational and long-tail queries (definitions, tutorials, general knowledge), precisely the traffic that blogs, knowledge bases, and affiliate sites depend on.  

The evidence is clear. Zero-click search is rapidly rising, and organic CTRs are falling wherever AI-powered answers are available. 

What CTRs miss in the AI search era? 

Traditional CTR metrics miss a big part of the picture: invisible brand exposure. Your brand may be mentioned in AI responses without generating a single click, highlighted in knowledge panels without direct attribution, or recommended through voice search on smart devices. Even AI-generated summaries from platforms like ChatGPT, Claude, Perplexity, and Gemini draw on your content. These shape user perception without leaving a measurable trail. 

The false correlation problem  

High CTR no longer equals high visibility in AI systems. Consider this example:  

• Brand A ranks #1 organically, receives 500 monthly clicks  
• Brand B gets cited in 50 AI Overview responses, receives 50 clicks  
• Traditional metrics favor Brand A, but Brand B influences thousands more users through AI  

This disconnect means businesses optimizing solely for CTR may miss massive audience reach in AI environments.  

These numbers confirm the trend. A large (and growing) chunk of search queries never leads to an external click, instead being resolved by AI/Google. This doesn’t mean all organic traffic is lost; many queries (mainly transactional, local, or brand-specific) still send clicks, but the landscape is clearly shifting toward answering directly. 

Six AI LLM optimization metrics

With traditional click metrics weakening, SEO must evolve. CTRs and ranks still matter, but they’re incomplete indicators now. We must measure how content performs within AI-generated answers, even when no one clicks. As Cyberclick observed, your content might be “cited, referenced, or sourced by AI systems”, which they call zero-click visibility, yet none of that shows up in Google Search Console or analytics. In other words, your page could be the knowledge behind an answer, building authority, without any direct traffic trace.  

To account for this, experts recommend new AI metrics: 

1. AI brand mention rate 

Definition: Frequency of brand appearances in AI-generated responses across major platforms (ChatGPT, Claude, Perplexity, Google AI Overviews).

This metric is critical because it has the strongest correlation with AI Overview visibility. The top 25% of brands receive over 169 monthly AI mentions, compared to just 14 for the next tier. Meanwhile, 26% of brands have zero AI mentions at all, revealing massive gaps and untapped opportunities in brand visibility. 

How to measure:

• Manual query testing across LLM platforms using brand-related searches  
• Custom monitoring scripts to track brand mentions in AI responses  
• Competitive benchmarking against industry leaders  

Optimization tactics:  

• Create quotable, cite-worthy statistics and insights that AI systems prefer  
• Build topical authority through comprehensive content coverage  
• Increase web mentions across trusted, high-authority sources  
• Develop thought leadership content that positions your brand as an expert source  

Pro tip: Yoast AI Brand Insights can help track and optimize your brand’s visibility across AI platforms, giving you actionable data to improve mention frequency and context. 

2. Semantic relevance score 

Definition: Measurement of content alignment with search intent through vector embeddings rather than keyword matching  

This metric is critical because AI systems rely on semantic similarity rather than exact keyword matches when selecting content. It predicts the likelihood of being included in AI-generated answers across different platforms and measures how accurately content aligns with queries beyond surface-level optimization. 

How to measure:  

• OpenAI Embedding API for content-query similarity scoring  
• Go Fish Digital’s Embedding Relevance Score tool for automated analysis  
• A/B testing content variations to identify the highest-scoring approaches  
• Topic clustering analysis to understand semantic relationships  

Optimization tactics:  

• Focus on comprehensive topic coverage rather than keyword density  
• Use entity-based content strategies that connect related concepts  
• Optimize for question-answer formats that AI systems prefer  
• Create contextually rich content that covers user intent fully  

Advanced strategy: Implement structured content hierarchies using clear H2/H3 sections that mirror how AI systems process information for responses. 

3. Structured data implementation score 

Definition: Percentage of pages with proper schema markup and AI-readable formatting  

This is critical because AI systems strongly favor structured, machine-readable data over plain text. Schema markup improves AI comprehension, boosts the chances of being cited, and enables rich snippet appearances that reinforce visibility alongside AI Overviews. 

How to measure:  

• Schema markup validation tools to audit implementation coverage  
• Percentage of key pages with relevant structured data types  
• Rich snippet appearance tracking across target queries  
• Technical SEO audits focusing on markup completeness  

Optimization tactics:  

• Implement FAQ and HowTo schemas for informational content  
• Use comprehensive schema types (Organization, Product, Service, Review)  
• Create clean, markdown-friendly content formats that AI can easily parse  
• Optimize internal linking structure to support entity relationships  

Note: Yoast SEO Premium includes advanced schema implementation features that can automate much of this optimization process.  

4. Citation quality index 

Definition: Quality weighting of attributed mentions and source links in AI responses  

This index is critical because it fuels both traffic and trust within AI recommendation systems. Quality citations strengthen brand authority in LLM training, while linked references deliver three times more value than unlinked mentions. 

How to measure:

• Track citations with proper source attribution across AI platforms  
• Monitor the authority scores of sites that cite your content  
• Measure click-through rates from AI citations when available  
• Assess citation context quality (positive, neutral, negative sentiment)  

Optimization tactics:  

• Create authoritative, research-backed content that merits citation  
• Build relationships with industry publications and thought leaders  
• Optimize content for “cite sources” inclusion with clear attribution  
• Develop proprietary data and insights that become go-to industry references  

Advanced tracking: Use tools like Brand24 or Mention.com to monitor unlinked brand citations that may influence AI training without generating trackable links.  

5. Query match coverage 

Definition: Breadth of related queries where your content appears in AI responses  

Query match coverage is essential because AI systems favor comprehensive topical coverage over a narrow focus. And broader query coverage indicates higher entity authority. It also predicts inclusion across multiple AI response types and platforms  

How to measure:  

• Topic clustering analysis to map query coverage  
• Competitive content gap analysis to identify opportunities  
• Query coverage mapping across your content portfolio  
• AI response monitoring for related search terms  

Optimization tactics:  

• Create pillar or cornerstone content with comprehensive topic coverage  
• Answer related questions thoroughly within single content pieces  
• Build content clusters around core topics using internal linking  
• Develop FAQ sections that address query variations  

Content strategy: Use tools like Yoast’s content optimization features to ensure your content covers topics comprehensively for AI visibility.  

6. AI positioning score  

Definition: Average placement position of your brand/content within AI-generated responses  

AI positioning score matters because earlier placement in AI responses gets far more attention. First-position mentions see up to three times higher engagement, and strong positioning directly boosts perceived brand authority. 

How to measure:  

• Track the mention position across AI responses manually  
• Calculate the average placement across multiple queries over time  
• Monitor position trends to identify optimization success  
• Benchmark positioning against direct competitors  

Optimization tactics:

• Optimize content for primary source citation by AI systems  
• Build first-party research and proprietary data that AI prefers  
• Create definitive resources that become category authorities  
• Focus on expertise signals (author credentials, source authors) 

Why CTR still matters (and how to optimize it) 

Even as AI visibility metrics rise in importance, CTR still plays a crucial role. Clicks directly drive conversions and sales, making them essential for revenue. A strong CTR also signals clear content-query alignment, which boosts overall visibility. Over time, pages with consistently higher CTR often gain better placement in AI-generated citations, which creates an advantage. 

CTR optimization in the AI era

Write for click-desire, not just keywords

Today, writing for click desire is more important than ever. Instead of focusing only on keywords, craft curiosity-driven headlines that promise insights users won’t find in AI summaries. Pair these with benefit-focused meta descriptions that highlight exclusive value, and tease proprietary data or tools that can only be accessed on your site. 

Enhanced SERP presentation

Equally important is how your content presents itself in the SERPs. Comprehensive schema markup can unlock rich snippets, while optimized title tags and emotionally engaging meta descriptions help your results stand out. Structured snippets are also powerful for showcasing your unique selling propositions directly on the results page. 

Mobile optimization

Finally, mobile optimization ensures that once users click, they stay engaged. Fast page load speeds provide immediate satisfaction, while scannable content structures make information easy to digest on smaller screens. Queries here often carry higher intent, making them a valuable source of qualified clicks.

The bigger picture: Generative SEO (GEO/AEO) 

Traditional SEO is shifting fast. With AI-driven search platforms like Google’s AI Overviews, ChatGPT, and Perplexity shaping results, businesses now need to optimize for Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO). 

In simple terms: Instead of fighting for clicks on SERPs, the new goal is to have your content chosen as trusted source material in AI-generated answers. 

What GEO/AEO means for your content 

AI-powered search engines “read” and select content differently from Google’s classic algorithm. They prioritize: 

• Clarity & structure → short, factual sentences 
• Explicit answers → direct responses to common queries 
• Scannable formats → helpful headings, bullet lists, and one idea per paragraph 
• E-E-A-T compliance → expertise, authoritativeness, trustworthiness 
• Credible sources → supported by citations 

How Yoast helps you optimize for GEO 

The Yoast SEO plugin includes features designed for this new search reality: 

• llms.txt generation → creates a “map” for AI assistants, highlighting your key content in plain text 
• Readability checks → sentence length and reading ease tools help you write concise, AI-friendly content 
• Green lights, simplified → signals that your content is structured for both humans and AI systems 

Want more? Learn how to optimize content for LLMs, and read all about our new llms.txt SEO feature. 

The role of branding in GEO 

Here’s what many miss: AI Overviews strip away logos, design, and slogans. All that remains is text. That means your brand identity must live in your words. 

To stand out in AI-generated answers: 

• Use brand-specific language and stories 
• Strengthen authority with schema markup and citations 
• Make sure your brand’s voice and expertise come through clearly 

This is where AI Brand Insights comes in. This feature will: 

1. Track how AI assistants mention your brand. 
2. Show how your business is represented in AI-generated answers. 
3. Help refine your brand narrative in the age of AI search. 

In short: GEO isn’t about SERP position alone; it’s about what AI “knows” and shows about your brand. 

Essential takeaways 

1. CTRs remain essential but insufficient for measuring true search success 
2. AI brand mentions and citation quality predict long-term visibility better than traditional rankings
3. Structured data and semantic optimization determine inclusion in the AI-generated responses
4. Multi-platform visibility tracking is essential as search behavior fragments across AI tools

Ready to optimize visibility in AI search? 

The transformation to AI-powered search is already here. Early adopters who implement comprehensive AI visibility measurement today will establish competitive advantages that build over time.  

Start tracking your AI mentions immediately using the frameworks outlined above. Audit your content for AI-friendliness and implement structured data optimization. Most importantly, build authority through comprehensive topic coverage and citation-worthy insights that position your brand as an industry authority across traditional search and AI platforms.  

The brands that thrive in the next decade will not be those with the highest CTRs; they will be the ones that understand how to build influence and visibility in an AI-first search world.  

👉 [Join the waitlist for AI Brand Insights] and be among the first to shape how AI sees your brand. 

Everything you need to know about AI citations vs backlinks

For years, backlinks have been the gold standard for building authority, driving link juice, and climbing up the SERPs. But with the rise of Generative AI, the search landscape is shifting. Instead of chasing endless links, visibility now also depends on something more intelligent: AI citations. This evolution means your brand can show up in front of wider audiences, even without a massive backlink profile.

The question is, when it comes to AI citations versus backlinks, how do they differ, and does one outweigh the other? In this blog, we’ll break down both, explore their role in building authority, and uncover whether AI citations are the future of digital visibility or just another layer to your SEO strategy.

What are backlinks?

Backlinks are simply links from one website to another. Think of them as digital recommendations: when a reputable site links to your content, it signals to search engines that your page is trustworthy and valuable.

For example, below is a screenshot from a Zapier blog post that links to the Yoast SEO plugin landing page in the blog.

Backlinks aren’t new; they’ve been around for more than two decades. In fact, links were introduced back in 1998 as part of Google’s original PageRank algorithm, making them one of the oldest forms of online citations. Since then, they’ve remained a core ranking factor, shaping how websites compete for visibility.

Today, backlinks are still considered one of the strongest signals for building authority. Many brands invest in link-building strategies to secure high-quality backlinks, from being cited in well-written pieces to building relationships that earn natural mentions.

Why backlinks matter?

Backlinks are not just about search rankings, but they influence almost every aspect of your website’s visibility and growth. Here’s why they remain essential:

• Improve rankings by acting as one of Google’s most important signals, especially when they come from authoritative domains
• Drive referral traffic that is often highly targeted and more likely to engage with your content
• Boost authority and credibility by showing search engines that trusted sites vouch for your content
• Help with faster indexing by guiding search engine crawlers to discover and prioritize your pages
• Provide semantic understanding by giving Google context through anchor text and linking page content

What types of backlinks work best?

Not all backlinks are equal, and the ones that matter most usually have these traits:

• They come from trusted and authoritative websites
• They include your target keyword or a variation of the target keywords in your anchor text
• They are topically relevant to your niche
• They are ‘dofollow’ links that pass link equity

Backlinks remain important for SEO, but as search evolves, they’re no longer the only way to build authority. This is where AI citations enter the picture.

Read more: Link building from a holistic SEO perspective

What are AI citations?

AI citations are references, attributions, or direct links to your content, brand, or product that appear within AI-generated answers. Unlike traditional backlinks that live inside web content, AI citations are shown within AI search results or summaries. They often appear as clickable source cards, numbered footnotes, or links listed below an AI overview.

For example, when Google AI Overviews quotes websites in the AI search box, it cites the original sources that provided the information.

Some other examples of AI citations are:

• ChatGPT cites your brand or content as part of its generated answer
• Bing Copilot highlights your product as a recommended solution to a user’s query, even if it doesn’t include a direct link
• Perplexity.ai lists your research as a supporting source beneath its summarized response

Why AI citations matter for visibility?

AI citations are becoming critical for brand exposure because they align with how people now consume information online:

• Search is becoming prompt-driven, which means users type questions or prompts instead of keywords. If AI picks your content to cite, you’re instantly visible to that audience
• Discovery is moving from clicks to context. Users may not always visit your website, but being cited ensures your brand becomes part of the answer itself
• AI is becoming your audience’s first impression. In many cases, people see the AI summary before they see the actual search results. Appearing as a cited source makes your brand part of that first interaction
• Citations boost credibility and authority. When an AI tool references your content, it signals to users that your site is trustworthy enough to be part of the response

Types of AI citations that influence brand visibility

Not all AI citations look the same. Here are the key forms that shape how your brand is discovered:

Name-drop mentions drive brand visibility

When AI directly mentions your brand or product in its response, such as in a recommendation or ‘best of list, you gain instant visibility in front of users without them needing to click further.

Source references build credibility signals

These citations work like the ‘works cited’ section in AI outputs. Tools like Gemini, Perplexity, or Google AI Overviews may display your URL in the list of sources at the bottom of the response. Even if you’re not in the main summary, you benefit from the authority signal.

Quoted passages establish expert authority

When AI pulls exact wording from your content and attributes it to you, it elevates your position as an expert. This type of citation places you in prime digital real estate, signalling leadership in your niche.

Synthesized mentions shape brand narrative

Sometimes AI blends your insights into its summary without naming or linking back to you. While harder to measure, your content still influences the narrative and reinforces brand authority in indirect ways.

AI citations are already reshaping how visibility works in search. Just as backlinks defined SEO two decades ago, citations in AI search are now shaping brand perception by influencing what users see, trust, and remember about your business.

How are AI citations and backlinks different?

So, now that we have an overview of AI citations and backlinks, let’s see how backlinks and LLM citations differ from each other -`

How to earn both?

Earning backlinks and AI citations doesn’t have to be two separate strategies. With the right approach, the same efforts that build traditional authority also make your content LLM crawler-friendly.

Here’s how to do it:

Create deep, original, and useful content

Go beyond rewriting what’s already ranking. Publish original research, case studies, interviews, or unique perspectives that others can’t find elsewhere. AI models pull from fresh, problem-solving content, and so do journalists and bloggers who link naturally.

Write for real questions, not just keywords

Search is shifting from keywords to prompts. Pay attention to what your audience is actually asking on forums, social media, and other platforms. Create conversational, direct answers to those questions. If your content aligns with user prompts, it’s far more likely to be both cited by AI and linked by humans.

Leverage structured data

Use schema markup (FAQ, HowTo, Article, Product) to help AI and search engines clearly understand your content. Proper attribution of authors and sources also increases your chance of being recognized as a credible reference. Structured, transparent content is ‘citation ready.’

Build relationships for natural backlinks

Backlinks remain relationship-driven. Connect with journalists, bloggers, and industry peers through guest posts, expert roundups, or collaborations. AI often mirrors human trust signals, so if authoritative voices link to you, AI is more likely to cite you too.

Focus on clarity and quotability

Make your content easy to lift and reuse. Use short, memorable statements, stats, or definitions that can be quoted word-for-word. Structured layouts like subheadings, lists, and bullet points make content easier to reference by both humans and AI.

Monitor, analyze, and adapt

Don’t just publish; instead, track performance. Use SEO tools for backlinks and platforms to monitor AI citations and understand AI brand perception. If competitors are cited for prompts you should own, study their structure and improve on it. Adjusting based on data helps you stay ahead.

The takeaway: With the right strategies, you don’t need separate plans for backlinks and AI citations. Clear, authoritative, and trustworthy content earns both and multiplies your visibility across search engines and AI-powered platforms.

Exploring Yoast’s AI features

Applying the right strategies for earning backlinks and AI citations is easier when you have the right tools. Yoast’s AI features combine SEO best practices with AI-powered enhancements to make your content clearer, more discoverable, and more effective.

Here’s how they can support your workflow:

Yoast AI Generate

Quickly create multiple, tailored titles and meta descriptions for your pages or blog posts. This ensures your content attracts clicks and stands out in search results. You can select from different options, tweak them to fit your brand voice, and preview how they’ll appear in SERPs.

Yoast AI Summarize

Turn long-form content into scannable, bullet-point takeaways in seconds. This may also help reduce bounce rates by giving readers immediate clarity on what your page delivers. It also makes your content easier for AI systems and Google’s AI Overviews to interpret correctly.

Yoast AI Optimize

Get AI-powered suggestions to improve SEO signals such as keyphrase distribution, sentence length, and readability. You can review, apply, or dismiss recommendations with one click, ensuring that optimization never comes at the cost of your unique editorial voice.

Together, these AI-powered features help you save time, improve clarity, and boost both human and AI-driven visibility, laying the foundation for stronger backlinks and more consistent AI citations.

Backlinks or citations: What truly matters for visibility?

Backlinks have been the backbone of SEO for more than two decades, helping websites climb rankings, build authority, and attract referral traffic. But the rise of AI citations is reshaping how visibility works. When AI systems like Google’s AI Overviews or ChatGPT cite your content, they place your brand directly in front of users at the moment of discovery.

The truth is, it’s not a choice between backlinks and AI citations. Both matter, but in different ways. Backlinks remain critical for SEO growth and authority, while AI citations are quickly becoming the new gatekeepers of brand perception and visibility. The winning strategy is to create content that earns both.

Ahad Qureshi

I’m a Computer Science grad who accidentally stumbled into writing—and stayed because I fell in love with it. Over the past six years, I’ve been deep in the world of SEO and tech content, turning jargon into stories that actually make sense. When I’m not writing, you’ll probably find me lifting weights to balance my love for food (because yes, gym and biryani can coexist) or catching up with friends over a good cup of chai.

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When AI gets your brand wrong: Real examples and how to fix it

We’ve all asked a chatbot about a company’s services and seen it respond inaccurately, right? These errors aren’t just annoying; they can seriously hurt a business. AI misrepresentation is real. LLMs could provide users with outdated information, or a virtual assistant might provide false information in your name. Your brand could be at stake. Find out how AI misrepresents brands and what you can do to prevent them.

How does AI misrepresentation work?

AI misrepresentation occurs when chatbots and large language models distort a brand’s message or identity. This could happen when these AI systems find and use outdated or incomplete data. As a result, they show incorrect information, which leads to errors and confusion.

It’s not hard to imagine a virtual assistant providing incorrect product details because it was trained on old data. It might seem like a minor issue, but incidents like this can quickly lead to reputation issues.

Many factors lead to these inaccuracies. Of course, the most important one is outdated information. AI systems use data that might not always reflect the latest changes in a business’s offerings or policy changes. When systems use that old data and return it to potential customers, it can lead to a serious disconnect between the two. Such incidents frustrate customers.

It’s not just outdated data; a lack of structured data on sites also plays a role. Search engines and AI technology like clear, easy-to-find, and understandable information that supports brands. Without solid data, an AI might misrepresent brands or fail to keep up with changes. Schema markup is one option to help systems understand content and ensure it is properly represented.

Next up is consistency in branding. If your brand messaging is all over the place, this could confuse AI systems. The clearer you are, the better. Inconsistent messaging confuses AI and your customers, so it’s important to be consistent with your brand message on various platforms and outlets.

Different AI brand challenges

There are various ways AI failures can impact brands. AI tools and large language models collect information from sources and present it to build a representation of your brand. That means they can misrepresent your brand when the information they use is outdated or plain wrong. These errors can lead to a real disconnect between reality and what users see in the LLMs. It could also be that your brand doesn’t appear in AI search engines or LLMs for the terms you need to appear.

At the other end, chatbots and virtual assistants talk to users directly. This is a different risk. If a chatbot gives inaccurate answers, this could lead to serious issues with users and the outside world. Since chatbots interact directly with users, inaccurate responses can quickly damage trust and harm a brand’s reputation.

Real-world examples

AI misrepresenting brands is not some far-off theory because it has an impact now. We’ve collected some real-world cases that show brands being affected by AI errors.

All of these cases show how various types of AI technology, from chatbots to LLMs, can misrepresent and thus hurt brands. The stakes can be high, ranging from misleading customers to ruining reputations. It’s good to read these examples to get a sense of how widespread these issues are. It might help you avoid similar mistakes and set up better strategies to manage your brand.

Case 1: Air Canada’s chatbot dilemma

• Case summary: Air Canada faced a significant issue when its AI chatbot misinformed a customer regarding bereavement fare policies. The chatbot, intended to streamline customer service, instead created confusion by providing outdated information.
• Consequences: This erroneous advice led to the customer taking action against the airline, and a tribunal eventually ruled that Air Canada was liable for negligent misrepresentation. This case emphasized the importance of maintaining accurate, up-to-date databases for AI systems to draw upon, illustrating a major AI error in alignment between marketing and customer service that could be costly in terms of both reputation and finances.
• Sources: Read more in Lexology and CMSWire.

Case 2: Meta & Character.AI’s deceptive AI therapists

• Case summary: In Texas, AI chatbots, including those accessible via Meta and Character.AI, were marketed as competent therapists or psychologists, offering generic advice to children. This situation arose from AI errors in marketing and implementation.
• Consequences: Authorities investigated the practice because they were concerned about privacy breaches and the ethical implications of promoting such sensitive services without proper oversight. The case highlights how AI can overpromise and underdeliver, causing legal challenges and reputational damage.
• Sources: Details of the investigation can be found in The Times.

Case 3: FTC’s action on deceptive AI claims

• Case summary: An online business was found to have falsely claimed its AI tools could enable users to earn substantial income, leading to significant financial deception.
• Consequences: The fraudulent claims defrauded consumers by at least $25 million. This prompted legal action by the FTC and served as a stark example of how deceptive AI marketing practices can have severe legal and financial repercussions.
• Sources: The full press release from the FTC can be found here.

Case 4: Unauthorized AI chatbots mimicking real people

• Case summary: Character.AI faced criticism for deploying AI chatbots that mimicked real people, including deceased individuals, without consent.
• Consequences: These actions caused emotional distress and sparked ethical debates regarding privacy violations and the boundaries of AI-driven mimicry.
• Sources: More on this issue is covered in Wired.

Case 5: LLMs generating misleading financial predictions

• Case summary: Large Language Models (LLMs) have occasionally produced misleading financial predictions, influencing potentially harmful investment decisions.
• Consequences: Such errors highlight the importance of critical evaluation of AI-generated content in financial contexts, where inaccurate predictions can have wide-reaching economic impacts.
• Sources: Find further discussion on these issues in the Promptfoo blog.

Case 6: Cursor’s AI customer support glitch

• Case summary: Cursor, an AI-driven coding assistant by Anysphere, encountered issues when its customer support AI gave incorrect information. Users were logged out unexpectedly, and the AI incorrectly claimed it was due to a new login policy that didn’t exist. This is one of those famous hallucinations by AI.
• Consequences: The misleading response led to cancellations and user unrest. The company’s co-founder admitted to the error on Reddit, citing a glitch. This case highlights the risks of excessive dependence on AI for customer support, stressing the need for human oversight and transparent communication.
• Sources: For more details, see the Fortune article.

All of these cases show what AI misrepresentation can do to your brand. There is a real need to properly manage and monitor AI systems. Each example shows that it can have a big impact, from huge financial loss to spoiled reputations. Stories like these show how important it is to monitor what AI says about your brand and what it does in your name.

How to correct AI misrepresentation

It’s not easy to fix complex issues with your brand being misrepresented by AI chatbots or LLMs. If a chatbot tells a customer to do something nasty, you could be in big trouble. Legal protection should be a given, of course. Other than that, try these tips:

Use AI brand monitoring tools

Find and start using tools that monitor your brand in AI and LLMs. These tools can help you study how AI describes your brand across various platforms. They can identify inconsistencies and offer suggestions for corrections, so your brand message remains consistent and accurate at all times.

One example is Yoast SEO AI Brand Insights, which is a great tool for monitoring brand mentions in AI search engines and large language models like ChatGPT. Enter your brand name, and it will automatically run an audit. After that, you’ll get information on brand sentiment, keyword usage, and competitor performance. Yoast’s AI Visibility Score combines mentions, citations, sentiment, and rankings to form a reliable overview of your brand’s visibility in AI.

Optimize content for LLMs

Optimize your content for inclusion in LLMs. Performing well in search engines is not a guarantee that you will also perform well in large language models. Make sure that your content is easy to read and accessible for AI bots. Build up your citations and mentions online. We’ve collected more tips on how to optimize for LLMs, including using the proposed llms.txt standard.

Get professional help

If nothing else, get professional help. Like we said, if you are dealing with complex brand issues or widespread misrepresentation, you should consult with professionals. Brand consultants and SEO experts can help fix misrepresentations and strengthen your brand’s online presence. Your legal team should also be kept in the loop.

Use SEO monitoring tools

Last but not least, don’t forget to use SEO monitoring tools. It goes without saying, but you should be using SEO tools like Moz, Semrush, or Ahrefs to track how well your brand is performing in search results. These tools provide analytics on your brand’s visibility and can help identify areas where AI might need better information or where structured data might enhance search performance.

Businesses of all types should actively manage how their brand is represented in AI systems. Carefully implementing these strategies helps minimize the risks of misrepresentation. In addition, it keeps a brand’s online presence consistent and helps build a more reliable reputation online and offline.

Conclusion to AI misrepresentation

AI misrepresentation is a real challenge for brands and businesses. It could harm your reputation and lead to serious financial and legal consequences. We’ve discussed a number of options brands have to fix how they appear in AI search engines and LLMs. Brands should start by proactively monitoring how they are represented in AI.

For one, that means regularly auditing your content to prevent errors from appearing in AI. Also, you should use tools like brand monitor platforms to manage and improve how your brand appears. If something goes wrong or you need instant help, consult with a specialist or outside experts. Last but not least, always make sure that your structured data is correct and aligns with the latest changes your brand has made.

Taking these steps reduces the risks of misrepresentation and enhances your brand’s overall visibility and trustworthiness. AI is moving ever more into our lives, so it’s important to ensure your brand is represented accurately and authentically. Accuracy is very important.

Keep a close eye on your brand. Use the strategies we’ve discussed to protect it from AI misrepresentation. This will ensure that your message comes across loud and clear.

Edwin Toonen

Edwin is an experienced strategic content specialist. Before joining Yoast, he worked for a top-tier web design magazine, where he developed a keen understanding of how to create great content.

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What AI gets wrong about your site, and why it’s not your fault: meet llms.txt 

AI tools are everywhere — from chatbots that answer customer questions to language models that summarize everything from documentation to legal text. But if you’ve ever asked a model like ChatGPT to explain your site, your product, or your API, the results might not feel quite right. In fact, sometimes they’re way off. And no, that’s not your fault. 

The disconnect between websites and LLMs 

Large language models (LLMs) like ChatGPT, Claude, or Gemini are trained to understand a wide range of content. But when they try to interpret your website at runtime, that is, when someone is actively asking them a question, they run into a few core problems: 

• HTML is noisy. Navigation bars, cookie banners, modal popups, and analytics scripts clutter the page. 
• Context windows are limited. Most websites are too large for an LLM to process all at once. 
• Important details are spread across multiple pages or hidden in tables, code blocks, or comments. 
• Markdown docs may exist, but the model often can’t locate them, or even know they exist. 

So, when you ask an AI tool to “explain what this company does” or “summarize this library API”, it often gets stuck. It either skips key context or grabs the wrong signals from cluttered markup. 

It’s not bad intent; it’s a design limitation. 

Why it’s not your SEO’s fault, either 

You’ve probably invested time and effort into search engine optimization. Maybe your robots.txt and sitemap.xml are in place. You’ve got meta tags, structured data, and clean internal links. Good, but LLMs don’t always work like Google. 

Traditional SEO helps your site get found. However, it doesn’t guarantee that AI tools will understand what a human user would. That’s where a new proposal comes in. 

Meet llms.txt: A simple way to help AI understand your site 

A growing number of developers and AI researchers are adopting a lightweight, human-readable standard called llms.txt.  

What is llms.txt? 

llms.txt is a plain Markdown file placed at the root of your site that provides language models with a summary of your project and direct links to clean, LLM-readable versions of important pages. It’s designed for inference-time use, helping AI tools quickly understand a site’s structure, purpose, and content without relying on cluttered HTML or metadata intended for search engines. 

What it does: 

• Gives a short summary of your site or project 
• Links to clean, LLM-ready Markdown versions of key pages 
• Helps AI tools find exactly what matters, without parsing messy HTML

Is it widely supported? Not yet 

Right now, no major LLM provider officially supports llms.txt. Tools like GPTBot (OpenAI), Claude (Anthropic), and Google’s AI crawlers don’t reference or follow it as part of their crawling behavior. Some companies like Anthropic publish llms.txt files themselves, but there’s no evidence that any crawler is actively using them in retrieval or training. 

Still, it’s a low-effort, no-risk addition that helps prepare your site for a future where structured LLM access becomes more standardized. And LLM-facing tools, or even your own AI agents, can make use of it today. 

Example use cases: 

• A dev library links to .md-formatted API docs and usage examples. 
• A university site highlights course descriptions and academic policies. 
• A personal blog offers a simplified timeline of key projects or topics. 

You control the content and the structure. LLMs benefit from curated, LLM-aware context. And users asking questions about your site get better answers. 

Using our Yoast SEO plugin? 

If you’re already using our Yoast SEO (free or Premium) plugin, generating a llms.txt file is easy. Just enable the feature in your settings, and the plugin will automatically create and serve a complete llms.txt file for your site. You can view it anytime at yourdomain.com/llms.txt. 

An LLM-friendly web isn’t the same as a Google-friendly web 

This doesn’t replace SEO. Think of llms.txt as a companion to robots.txt. It tells AI bots: “Here’s the good stuff. Skip the noise.” 

Sitemaps help crawlers find everything. llms.txt tells LLMs what to focus on. 

It’s especially useful for: 

• Developers and open-source maintainers 
• Product marketers looking to reduce support load 
• Teams that want chatbots to pull answers from docs, not guess 

You don’t need a new CMS or tech stack 

All this requires is creating two things: 

1. A basic llms.txt file in Markdown
2. Ideally, you’d also have Markdown versions (.html.md) of key pages included alongside the originals, with the same URL plus .md added. 

No new tools, plugins, or frameworks needed, although some ecosystems are already adding support. 

Here’s an example of a file automatically built by Yoast SEO, as it has an llms.txt generator built in:

Generated by Yoast SEO v25.3, this is an llms.txt file, meant for consumption by LLMs. This is the [sitemap](https://everydayimtravelling.com/sitemap_index.xml) of this website. 
 
# everydayimtravelling.com: Stories from our travels 
 
## Posts 
- [Test video](https://everydayimtravelling.com/test-video/) 
- [A Journey Through Portugal’s Wine Country: A Suggested Wine Tour Route](https://everydayimtravelling.com/a-wine-tour-through-portugal/) 
- [Travel essentials for backpackers FAQ](https://everydayimtravelling.com/travel-essentials-for-backpackers-faq/) 
 
## Pages 
- [Checkout](https://everydayimtravelling.com/checkout/) 
- [Contact us](https://everydayimtravelling.com/contact-us/) 
- [How we started this blog](https://everydayimtravelling.com/pagina-harry-potter/) 
- [My account](https://everydayimtravelling.com/my-account/) 
- [Cart](https://everydayimtravelling.com/cart/) 
 
## Categories 
- [Europe](https://everydayimtravelling.com/category/europe/) 
- [Asia](https://everydayimtravelling.com/category/asia/) 
- [South America](https://everydayimtravelling.com/category/south-america/) 
- [Food](https://everydayimtravelling.com/category/food/) 
- [Western Europe](https://everydayimtravelling.com/category/europe/west-europe/) 
 
## Tags 
- [Budget](https://everydayimtravelling.com/tag/budget/) 

Helping AI help you 

So, if AI is misinterpreting your website, producing erroneous summaries, or skipping critical content, there’s a reason, and it’s fixable. 

It’s not always your copy. Not your design or your metadata. It’s just that these language tools need a little guidance. In the future, llms.txt could be the way to give it to them, and you do so on your terms. 

Do you need help creating an llms.txt file or converting your existing content to Markdown for LLMs? Yoast SEO can automatically generate an llms.txt file for you. 

Edwin Toonen

Edwin is an experienced strategic content specialist. Before joining Yoast, he worked for a top-tier web design magazine, where he developed a keen understanding of how to create great content.

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Integrating human insight with AI-generated content: How to maintain E-E-A-T

Now that so many people use AI tools to create content, the questions about the credibility of those tools keep popping up. Can you really make AI-generated content and still meet Google’s E-E-A-T standards? Of course, the answer is yes, but there’s a limit to what you should let these tools do. Incorporating human insights in AI content can help uphold these standards.

AI helps you move faster and do more, but it can’t replace humans (yet). Do you want readers to trust your content and have it seen as a reliable source in traditional and AI-driven search? Then, you need to have people involved in every stage of the content production process.

In this article, we’ll discuss how to combine AI content with human editing to maintain experience, expertise, authoritativeness, and trustworthiness. But we’ll also discuss what happens if you don’t do that.

AI can help you start, but humans make it credible

AI tools like ChatGPT, Claude, and Gemini are trained on enormous data sets. These tools are very good at outlining topics, summarizing facts, and writing initial, high-level drafts of articles. However, the benefits stop there, and going much further will present a risk.

You must remember that AI does not have the intent, context, or experience in your industry. With all the low-quality content that’s spit out daily, that matters more than ever. Google, using the AI Overviews and AI Mode, is trying to surface content that shows real insights from real people.

But why does human involvement matter so much? AI is great, but it often misses nuances and is prone to add filler to your content. It’s also very good at oversimplifying topics. And, because of the way these systems were taught, they cannot pick up evolving best practices or shifts that happen in the real world.

What’s more, if you let the AI run wild, it can even produce content that’s factually wrong. These hallucinations are so confidently written that they sound like they are true, which makes it harder to detect misinformation.

What to do?

It’s fine to use AI, but use it to help you structure content or brainstorm, and don’t publish anything directly. Always use real editors with real knowledge of the topics to fact-check, correct the tone, and make sure the message is on point. This helps you improve trustworthiness in E-E-A-T. You should show that you wrote your content with good intent and oversight.

Our Principal SEO, Carolyn Shelby, wrote The Role Of E-E-A-T In AI Narratives: Building Brand Authority For Search Success for Search Engine Journal. That article provides more insight into this topic.

Carolyn also wrote an insightful post on how to optimize content for AI LLM comprehension using Yoast’s tools.

Relying too much on AI can lead to risk

Remember that AI-generated content is not perfect. In fact, if you use it without having actual people working on it, it could hurt your visibility or reputation. In the end, this could hurt your business. But what are some of those risks when you over-rely on AI content?

False authority and misinformation

Search online and you’ll find many stories describing how AI wrote things that are just plain wrong. AI can misstate facts, make up statistics, and even come up with well-known experts that don’t exist. Publishing content like this in your brand’s name can damage your trustworthiness. What’s more, when search engines or visitors lose trust, it’s very hard to regain that.

Outdated or incomplete information

While there are many developments on this front, with grounding/RAG and LLMs connected to search, most models aren’t updated in real-time. These models often don’t know the latest insights until you specifically tell them. It’s easy to create outdated AI content when you don’t keep a very close eye on this.

Content redundancy

As you know, AI tools get data from existing sources, which will lead to content that looks a lot like content that’s already out there. If your content only repeats those same things, it’s very easy for search engines to ignore your site. It will be hard for Google to see your site as an authority on the topic.

Legal and compliance issues

There are many topics and industries that are very risky to publish on, for instance, the medical, financial, and legal fields. If your AI tool spits out incorrect advice and you publish without a human doing the fact-checking, your business could be found liable in court.

Trust breakdown with your audience

Remember that your readers are also developing a nose for AI content. When they sense that something sounds too generic or disconnected, they might move on to your competitor’s content, if that’s real. This will especially hurt industries that thrive on expertise and trust.

Add experience to strengthen the E’s

The biggest update of E-E-A-T was the addition of Experience. This is Google’s way of recognizing content created by people who have done or experienced what they wrote about. AI does not have this experience; real human beings do.

So, how do you do this? Be sure to include real stories from your team, clients, or projects, ideally with real names, results, and lessons learned. Give internal experts, such as engineers, consultants, or practitioners, a voice and direct input in your content. Don’t forget to interview team members and customers and use their perspectives in your content.

Giving your content more context can also make it stand out more, even in AI search. For instance, instead of simply writing “Solar panels reduce energy bills,” write, “After installing 28 commercial panels, our client in Portland, Oregon, cut annual costs by 35% — enough to pay off the system three years early.”

Make it easy for Google (and your audience) to trust you

Google’s systems, including AI Overviews and AI Mode, look at a lot more than just the words on your page. Google looks at all of the signals surrounding your business and yourself. These signals can help it understand if you and your content are trustworthy.

Improving your credibility signals for users and search engines starts by adding clear bylines with author bios that link to real credentials. This way, it’s easier to find out who is behind the content and why it makes sense for them to write about the topic. Support this with proper structured data, like schema markup for authors, products, reviews, and what else makes sense. Search engines use this to understand your content.

Remember to cite high-quality sources when referring to data instead of vague phrases like “research shows.” Also, set up a system to gather and use reader feedback so you can immediately fix things when they are unclear or plain wrong. Try everything to build and maintain trust while keeping content quality high.

Keep an eye on your Knowledge Graph. Try to get your brand and your experts or owners recognized as entities in search through structured data, Wikidata, Google Publisher Center, or by getting other citations. Think of authority and trust in E-E-A-T as something more visible, both to users and large language models (LLMs).

Always show who’s behind the content

AI content isn’t “real”. You, as a writer, are real. The best way to make your content real is by showing who wrote or reviewed it. Plus, you should show what makes them qualified to write about it. Transparency supports user trust and sets content apart from generic, anonymous posts.

Now, you don’t need a PhD from Harvard to be recognized as an expert for E-E-A-T, but you do need real-world, verifiable experience. In addition, you should publish author bios on your site with specific roles and industry backgrounds. You can also add an editorial or “reviewed by” credit for topics that your experts have fact-checked and edited.

Many big publishers have content guidelines and/or review policies that are available to read at any time. In those guidelines, you might have something simple, like what kind of disclosure you use when you’ve used AI to create a piece of content. That might be something simple like: “This article was drafted using generative AI and reviewed by [Editor Name], [Job Title] at [Company Name].”

Final thoughts

AI is a helpful tool for quickly generating content, but it shouldn’t replace real experiences, insights, or proper editing. Without the human element, you’ll miss the quality and trustworthiness needed to succeed with your content.

If you want your brand to be mentioned in AI search results and stand out amongst the competition, you need to make it clear that there are real people behind this content — real people with real knowledge and experiences.

Feel free to use AI where it can to speed up your work. But do make sure that the essential parts that your readers and search engines will value most are always human.

Edwin Toonen

Edwin is an experienced strategic content specialist. Before joining Yoast, he worked for a top-tier web design magazine, where he developed a keen understanding of how to create great content.

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